Gnss positioning using pressure sensors

ABSTRACT

Systems, methods and devices for improving the accuracy of GNSS data are provided. Specifically, embodiments of the invention can advantageously use sensor input to improve the accuracy of position fixes. The use of physical sensors in navigation systems is deemed particularly advantageous, especially where altitude data derived from the pressure sensor is calibrated with and/or blended with GNSS altitude data.

BACKGROUND OF THE INVENTION

The embodiments of the present invention relate generally to theimprovement of GNSS data using altitude data derived from a pressuresensor. Certain embodiments use pressure-sensor derived data ascalibrated by GNSS data and/or as blended with GNSS data to provide animproved altitude determination.

Further embodiments of the invention generally relate to detection, useand/or mitigation of multipath signals for communication and especiallynavigation systems. Certain embodiments further relate to the detection,use and or mitigation of multipath signals for GNSS systems.

There is currently a need for improved accuracy of GNSS positioning inparticular with respect to portable navigation devices providing mappingservices to users. Currently, GNSS horizontal accuracy is not sufficientto provide an accurate user map location without using “map matching”techniques—that is, without guessing a user location based on theposition of roads and other landmarks in the map, under the assumptionthat the user is constrained to a position within the road. Furthermore,current techniques are not sufficiently accurate to differentiatebetween closely spaced roads or stacked road conditions, for exampleunderneath overpasses or where two roads are in an upper deck-lower deckconfiguration. There is thus a need to improve the accuracy of GNSSpositioning systems to overcome these difficulties.

Furthermore, wireless communications systems generally experience aneffect known as “multipath”. “Multipath” refers to the reception of anon-line of sight signal from a signal source. Multipath signals mayresult, for example, from the reflection of a signal from a nearbyreflector, such as the ground, a building face or the surface of a bodyof water. Multipath signals may also result when signals aresignificantly refracted. In general a non-reflected or refracted(straight line) signal is referred to simply as the “signal”, “truesignal” or “line of sight signal”, whereas a reflected or refractedsignal is referred to as a multipath signal. Since the multipath signaldoes not travel along the line of sight, it always arrives at thereceiver later than the true signal.

Multipath signals, although sometimes useful, are often detrimental tosignal quality. As an example, if the chipping frequency of a rangingsignal is approximately 1 MHz, a multi path signal that travels along areflected path with an additional 300 m length will arrive one chiplater. When superimposed on the true signal, it distorts the receivedsignal quality and can in some cases result in a complete loss ofsignal.

Multipath problems are also detrimental to navigation systems,especially those that rely on the time of reception of received signals,such as used in Global Navigation Satellite Systems (GNSS) such as theGlobal Positioning System (GPS) or Galileo. FIG. 1, although anillustration of embodiments of the invention, and not of prior art, canhelp illustrate the detrimental effect of multipath signals innavigation schemes that rely on the time of reception of a signal.

FIG. 1 illustrates a two-dimensional satellite navigation case. Areceiver 100 is located at a point 100 on the surface 110 of the Earth,which is marked in this case with the arrow indicating the “trueposition” of the receiver 100. The receiver 100 receives signals fromsatellites 102 and 104. In the two dimensional case where the receivertime with respect to satellite system time is accurately known, the timeof reception of the two satellite signals is sufficient to compute atwo-dimensional position fix. Receiver 100 is in an “urban canyon”environment, surrounded by buildings 106 and 108.

As illustrated in FIG. 1, receiver 100 is able to receiver a line ofsight signal 112 from satellite 104, but line of sight signals 114 fromsatellite 102 are blocked by building 106. Receiver 100 receives insteadmultipath signals, here shown as a single signal 116 from satellite 102.Since the multipath signal 116 travels a longer distance to receiver 100than the line of sight signal would have traveled, receiver 100 recordsa later time for the time of arrival of the signal.

The computed position 118 of the receiver based on the time of arrivalof the signals of satellites 102 and 104 occurs at one of theintersection points of two circles defined with centers at thesatellites and respective radii equal to the measured distance of thereceiver 100 from the satellite. Because receiver 100 measures a greaterdistance from satellite 102 than the actual distance to satellite 102,the relevant intersection point is calculated lower than and to theright of receiver 100's true position.

In the past, multipath mitigation strategies have focused on signalprocessing techniques, such as the rejection of late arriving signals orthe analysis of signal shape. These processing techniques are,unfortunately, limited in their ability to accurately remove the effectsof multipath from the positioning solution provided by navigationreceivers. Furthermore, traditional techniques have not been able todetermine whether a line of sight signal is present, or whether onlymultipath signals are present. Thus, improved methods, systems anddevices are called for.

SUMMARY OF THE INVENTION

Certain embodiments of the invention relate to a navigation receiver,comprising: a pressure sensor input; circuitry coupled to the pressuresensor input; wherein the circuitry is configured to use informationfrom the pressure sensor input to improve a position fix. The navigationreceiver may further comprise a pressure sensor connected to thepressure sensor input; wherein the relative measurements of the pressuresensor are more accurate than the absolute measurements of the pressuresensor. Optionally, the navigation receiver may comprise a GNSSreceiver, wherein the circuitry is configured to calibrate an altitudederived from the pressure sensor using an altitude derived from the GNSSreceiver. Further, the navigation receiver may be so configured that thealtitude derived from the GNSS receiver used to calibrate the altitudederived from the pressure sensor has not been Kalman-filtered.Preferably, the circuitry comprises a central processing unit executinga position solution section, a velocity solution section, a Kalmanfilter and an atmospheric model section. Additionally, the circuitrycoupled to the pressure sensor input may be configured to derive a firstaltitude from the pressure sensor input; and the circuitry coupled tothe pressure sensor may be configured to blend the first altitude with asecond altitude received from a GNSS sensor. Preferably, the circuitrycoupled to the pressure sensor input is configured to carry out acomplementary filter operation.

Further embodiments of the invention relate to a method for improvementof GNSS positioning, comprising: receiving a GNSS measurement from aprimary GNSS system; deriving a first altitude from the GNSSmeasurement; deriving a second altitude from a pressure sensor inputcorresponding approximately in time to the GNSS measurement; andcombining the first and second altitude measurements to obtain a blendedaltitude measurement. Optionally, the step of combining the first andsecond altitude measurements comprises executing a complementary filteroperation. Preferably, the step of executing a complementary filteroperation comprises filtering data related to the first altitude using alow-pass filter and filtering data related to the second altitude usinga high-pass filter. Additionally, the second altitude can be derivedusing an atmospheric model.

Further embodiments of the invention relate to a portable navigationsystem, comprising: a GNSS sensor; a pressure sensor; and amicroprocessor configured to use input derived from the GNSS sensor andthe pressure sensor to provide a map location without usingmap-matching. Additionally, the portable navigation system may providehorizontal an accuracy of 10 meters or less in an urban canyonenvironment.

Optionally, the portable navigation system further comprises amachine-readable medium comprising software instructions that, whenexecuted by the microprocessor, would perform a method comprising thesteps of: computing a GNSS altitude; computing an altitude based on ameasurement derived from the pressure sensor; and combining the GNSSaltitude and the altitude based on a measurement derived from thepressure sensor to produce a blended altitude. The step of combining theGNSS altitude and the altitude based on a measurement derived from thepressure sensor preferably comprises using a complementary filter.

Still further embodiments of the invention relate to a portable vehiclenavigation system, comprising: a GNSS sensor; a pressure sensor; and amicroprocessor configured to use input derived from the GNSS sensor andthe pressure sensor to provide a map location; and wherein the portablenavigation system can differentiate between two roads in a stacked roadcondition. Furthermore the vehicle navigation system optionally providesa vertical accuracy of 10 meters or less under stacked road conditions.

Additionally, the vehicle navigation system further comprises amachine-readable medium comprising software instructions that, whenexecuted by the microprocessor, would perform a method comprising thesteps of: computing a GNSS altitude; computing an altitude based on ameasurement derived from the pressure sensor; and combining the GNSSaltitude and the altitude based on a measurement derived from thepressure sensor to produce a blended altitude. Preferably, the step ofcombining the GNSS altitude and the altitude based on a measurementderived from the pressure sensor comprises using a complementary filter.

Certain further embodiments of the present invention relate tonavigation receivers, comprising: a physical sensor input; circuitrycoupled to the physical sensor input; and wherein the circuitry isconfigured to use information from the physical sensor input formultipath mitigation. Such navigation receivers can be furtherconfigured such that the physical sensor input is adapted to beconnected to a physical sensor, wherein the relative measurements of thephysical sensor are more accurate than the absolute measurements of thephysical sensor; wherein the physical sensor is a pressure sensor;and/or wherein the physical sensor input is adapted to be connected toan inertial sensor.

In certain embodiments the circuitry comprises central processing unitexecuting a position solution section, a velocity solution section, aKalman filter and an atmospheric model section. Additionally, thenavigation receiver can be configured such that the circuitry coupled tothe physical sensor input is configured to derive first position-relateddata from the sensor input; and wherein the circuitry coupled to thephysical sensor is configured to blend the first position-related datawith second position-related data received from a GNSS sensor. Thenavigation receiver can be configured such that the circuitry coupled tothe physical sensor input is configured to carry out a complementaryfilter operation.

Embodiments of the invention further relate to a method for multipathmitigation, comprising receiving a GNSS measurement; calculating aposition based on the GNSS measurement; receiving a firstposition-related datum derived from a physical sensor input andcorresponding approximately in time to the GNSS measurement; determiningwhether the position based on the GNSS measurement was significantlyaffected by multi path by using the first position-related datum Themethod can be extended to include situations wherein the step ofdetermining whether the position based on the GNSS measurement wassignificantly affected by multipath further comprises using a secondposition-related datum derived from the physical sensor input andcorresponding to a time previous to the time of the GNSS measurement.Furthermore, the method can be performed such that the step ofdetermining whether the position based on the GNSS measurement wassignificantly affected by multipath comprises computing a differencebetween the first position-related datum and the second position-relateddatum. Preferably, the first position related datum is an altitude or anacceleration and optionally the altitude is calculated using anatmospheric model.

In certain embodiments, the method will further comprise the step offiltering the difference between the first position-related datum andthe second position-related datum with a complementary filter having asan input a corresponding position-related datum derived from GNSSsignals. Optionally, the complementary filter comprises a high-passfilter and a low-pass filter.

Other embodiments relate to a portable navigation system, comprising aGNSS sensor; a physical sensor; and a microprocessor configured to useinput derived from the GNSS sensor and the physical sensor to performmultipath mitigation. The portable navigation system can be configuredsuch that the physical sensor is a pressure sensor, and the systemcomprises a machine-readable medium comprising software instructionsthat, when executed by the microprocessor, perform a method comprisingthe steps of computing a GNSS position; computing a position changebased on a measurement derived from the physical sensor; and comparingthe position change against a threshold to determine whether the GNSSposition is likely to be significantly affected by multipath. Theposition change preferably comprises a change in altitude; and themethod advantageously further comprises augmenting the GNSS positionwith altitude information derived from the physical sensor through theuse of a complementary filter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a two-dimensional GNSS multipath problem and aspectsof preferred embodiments.

FIG. 2 is a GPS block diagram indicating a GPS system architectureaccording to certain embodiments.

FIG. 3 is a graph showing the performance of a traditional GPS receiverunder stacked road conditions.

FIG. 4 is a graph showing the performance of an embodiment of theinvention under stacked road conditions.

FIG. 5 is another graph illustrating the performance of an embodiment ofthe invention under stacked road conditions.

FIG. 6 is a system diagram showing a two Kalman-filter embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Numerous embodiments of the present invention relate to improving theaccuracy of position fixes and the concept of “multi path mitigation” innavigation systems. As used herein, “multipath mitigation” means alessening of detrimental effects of multipath signals.

In general, embodiments of the invention accomplish improved accuracyand multipath mitigation by using information, which may be any kind ofinformation, usually encoded as a digital or analog signal, derived froma sensor that provides information from a source other than the primaryGNSS system being used for position fixes. For example, if the sensor isprovided to supplement a Galileo receiver, the sensor may be based on aGPS receiver. Here, the term “derived from” means “retrieved as thedirect output of’ or “modified from the output of’. The sensor may alsobe a “physical sensor”. A physical sensor is a device that senses themore immediate physical environment of the receiver, as opposed to asensor that detects wireless signals broadcast by distant transmitterssuch as navigation satellites or remote pseudo-lites. Examples ofphysical sensors can include inertial sensors, altimeters, speedometers,etc.

A number of inventive concepts will become clear if explained inreference to a receiver embodiment that employs a pressure sensor tomonitor altitude and uses information about the altitude to improve itsposition fix calculations. The pressure information relayed by thesensor provides physical information about the position and displacementof the receiver between GPS fixes. If multi path is experienced by theGPS section of the receiver, the calculated position will significantlyvary from one fix to another. The fix will shift significantly invertical and horizontal directions. The instantaneous shift (meaning theshift between two tens fixes adjacent in time, here assumed to be 1second apart) can be in the order of meters horizontal and vertical. Thesudden jump in position from one second to the other represents anacceleration incompatible with the actual acceleration capabilities ofthe user using the receiver. The difference between calculated GPSvertical position and altitude as measured by the pressure sensor isused as a multi path detection mechanism. In other embodiments, the GNSSposition fixes are filtered using sensor data to improve their accuracyand to mitigate the effects of multipath and other errors.

This concept is illustrated in FIG. 1, which shows a two dimensionalnavigation system problem as described in the Background section. If thereceiver 100 is in motion, as would be the case for a pedestrian orvehicle, it will experience sudden emphasis on multipath signals basedon line-of-sight signal blockage. For example, if the receiver 100 ismoving from building 108 in the direction of building 106, it will atsome point experience a blockage of line-of-sight signal 114 and thereception of multipath signal 116. This can lead to a sudden shift inthe calculated position of the receiver, for example, from the trueposition of the receiver 100 to the computed position 118.

Physical sensors can be used to detect these sorts of multipath effects.If the user were physically moved from a static position to a position40 meters away within one second, and if one makes the assumption of aconstant positive acceleration in the first half of the one secondinterval, and a negative constant acceleration in the second half of theone second interval, to come to rest after one second, the constantacceleration should be 160 m/s2 or +16 G for 0.5 s and −16 G for theremaining 0.5 s, with a maximum velocity of 288 km/h at 0.5 seconds.Such acceleration is highly unlikely for any receiver that is associatedwith the location of a human being. Thus, appropriate for any particularapplication, a threshold change can be chosen that can be used todetermine whether or not a particular GNSS position fix is likely to besignificantly affected by multipath. The choice of the threshold candepend, of course, on the expected velocity of the receiver asillustrated in the above example. Preferred embodiments will compare acalculated GNSS displacement with a vertical displacement measured, forexample by means of a pressure sensor.

Currently available pressure sensors using, for example, MEMS (Microelectromechanical Systems) technology, have a resolution level inequivalent altitude of about 0.2 meters. One example of such a sensor isthe SMD-500 manufactured BOSCH Sensortec. Such sensors have a relativelygood dynamic response, sufficient to track stair climbing for example.It is generally possible using MEMS technology to determine the currentfloor of a user.

While pressure information is continuously varying due to weatherconditions, the time constant of this variation is in tens of minutes.Pressure sensor information can thus be calibrated by the GNSSinformation. This is a case of absolute accurate information, but withhigh noise level (GNSS information) versus relative information, butwith a quite accurate relative displacement (pressure derived altitudeinformation). That is, GNSS positioning has a lower accuracy, butproduces a position relative to known Earth-centered Earth-fixedcoordinates. A pressure sensor produces a pressure relative to astandard which is slowly varying, but which can be assumed to be thesame between any two GNSS fixes. The blending of both types can be done,for example, by a complementary filter or by a Kalman Filter. This willproduce a more accurate altitude component of position which can be usedto improve the overall fix.

In preferred embodiments, the pressure sensor will be calibrated usingaltitudes calculated directly from single position fixes rather thanKalman-filtered altitudes. The Kalman-filtered altitudes have lowernoise, but some bias due to the smoothing effect over many measurements.The pressure sensor, on the other hand, has inherently low intersamplenoise, but provides only relative accuracy. It is therefore notadvantageous to sacrifice absolute accuracy for better noisecharacteristics in the calibration standard.

The same technique, i.e. detecting the discrepancy between GNSS reportedposition and sensor-reported information over short intervals formultipath mitigation, can be used with other sensors, the most naturalbeing accelerometers. Certain additional difficulties are createdthereby, including that an accelerometer in some embodiment would needextra attitude information. In other embodiments, an accelerometer canbe used to determine whether the average acceleration regardless ofdirection is consistent with an acceleration as determined by a GNSSsystem.

A pressure sensor, in contrast has a natural assumed displacementmeasurement direction (vertical). Accelerometers could be used to detectspurious acceleration, but the conversion of acceleration into adisplacement equivalent is difficult. There may also be some spuriousexception conditions, for example, when the accelerometer detects animpact shock. Also, vertical acceleration will be much less thanhorizontal one, so it will generally be more difficult to detect betweentrue and false acceleration.

In certain embodiments, a pressure sensor can be calibrated using GPSinformation to derive an accurate altitude. This pressure sensorcalibration is not necessary for multi path mitigation, as there one isinterested in the sudden, relative variations or lack thereof in thealtitude, but calibration can be done where absolute altitudemeasurements are desirable. In order to avoid the “pollution” of thepressure sensor calibration with multipath effects, the calibration hasa very long time constant (in the order of 30 to 100 seconds minimum).That is, calibration (or setting of the baseline pressure relative towhich displacements are measured) takes place over many GNSS fix cycles.A receiver using a pressure sensor system can also be configured todisable or suspend the calibration operation if the GPS position isrecognized as being of low quality, for example, through a determinationthat pseudorange residuals are too high, or that vertical residuals aretoo high. In such a case, the operation can be configured to coast,using previous calibration values.

In practice, usually only one satellite at a time will be afflicted bytransitory multipath. The above-described techniques can detect whichsatellite is at fault, simply by measuring the residual of eachsatellite as compared with the propagated position from theinertial/pressure sensors. This allows the receiver to make the positionfix more accurate by rejecting measurements from the satellite afflictedwith multipath or by coasting previous values.

Alternatively, an extra pseudorange error for one satellite can bemeasured against the data derived from a physical sensor, at theaccuracy of the physical sensor. This allows a check of every satellitefor accuracy against this reference, without depending on group-basedtechniques such as RAIM (Receiver Autonomous Integrity Monitoring).

FIG. 2 illustrates using a block diagram a receiver architecture 200according to an embodiment of the present invention. FIG. 2 has a GNSSsensor 202, shown here as a GPS sensor, which acquires and trackssatellites and produces information relating to ranging measurements.Receiver architecture 200 also comprises a sensor 218, shown here as apressure sensor. Data derived from the GPS sensors and pressure sensorsare filtered with a two-state Kalman filter 234 and a complementaryfilter 244 in the manner described below. It will be apparent that thefilter embodiments as described herein can be significantly rearrangedwhile still performing a function that will achieve the result intendedby this disclosure.

GPS sensor 202 generates GPS pseudorange measurements at 204 and GPSdelta pseudorange measurements at 206. The GPS pseudorange measurementsare passed to a position solution section 208, which can be aleast-squares type position solution algorithm as shown. Similar to thepseudorange measurements, delta pseudorange measurements are passed at206 to a velocity solution section 214, which may be a least squaresvelocity solution section as shown. Here, the term “section”, refersgenerally to code, circuitry or both configured to perform a certainfunction, usually being identifiable as a method, object or logicalgrouping of software instructions. If implemented as code, a sectionwill generally be executed by an integrated circuit having a centralprocessing unit, such as a microprocessor, microcontroller orapplication specific integrated circuit with an embedded CPU core.Software instructions for such sections are embedded in a machinereadable medium, which can comprise, for example, a non-volatile memorysuch as Flash memory as an integrated circuit chip or portion of a chip.

Position solution section 208 outputs pseudorange vertical residuals at210 and GPS-calculated altitude at 212 (shown as point A). The output210 is connected to threshold section 242. Threshold section 242determines whether the vertical pseudorange residuals are withinacceptable limits, and if not, stops the input of data to Kalman filter234 using switches 238 and 236, which are preferably the calculatedvertical software functions. The inputs can be suspended if pseudorangedifferences exceed a certain threshold, as calculated by thresholdsection 242, i.e. if it is likely that multipath has reduced theaccuracy of a particular measurement to a significant extent. Thisallows the Kalman filter to coast and prevents the filter, andultimately atmospheric model section 226, from being too heavilyinfluenced by multipath distorted position fixes. GPS altitude output212 is also differenced at 232 with a barometric altitude output 227from atmospheric model section 226.

The resulting difference is fed to an input of Kalman filter section234, here shown as a two-state Kalman filter, over switch 238. Here theterm “input”, when used as a noun, means connection suitable forconveying information, such as a software function, or equivalenthardware such as a wire or interconnect, or a group of wires orinterconnects arranged as a bus, for example. To improve the accuracy ofthe system, the Kalman filter section 234 is used to estimate P₀ andT_(grad) which are parameters that can be used by atmospheric modelsection 226. The Kalman Filter observables are, for example: (1)altitude difference between the pressure sensor and GPS; and (2) thealtitude variation difference between pressure sensor and verticalvelocity (delta range), between two fixes. The internal states are: (1)P₀, the pressure at mean sea level (2) T_(grad), the lapse in degreesCelsius/km. The process model of P₀ and T_(grad) is a random walk, witha time constant to be adjusted on the weather conditions. Of course,Kalman filters with more or fewer states are possible, as areembodiments with no Kalman filters.

Variation in GPS altitude is output at 216 from velocity solutionsection 214 and differenced with barometric altitude differences 229 at230. The resulting double difference is input at 236 to Kalman filtersection 234. The altitude difference is generated by the output ofpressure sensor 218 by passing it at 220 to a decimating filter 222, theoutput of which is in turn passed at 224 to an atmospheric the model 227is differenced with a singlemodel section 226. The series output ofstage delayed version of itself 228 to produce barometric altitudedifferences.

The standard atmosphere defined by the document US Standard Atmosphere1976 (NOAA/NASA) “NOAA SIT 76-1562” can be used as a starting point foratmospheric model section 226. It defines a hydrostatic formula thatrelates atmospheric pressure and altitude, introducing two parameters,the pressure at sea level (P₀=1013 hPa), and the lapse, or temperaturevariation with altitude (T_(grad)=6.5 degrees C/km). The GPS altitudecan be used for calibrating these two parameters the barometricaltitude. It should eventually, significantly improving the accuracy ofbe noted thereby that barometric altitude and GPS altitude do not usethe same reference. Barometric altitude is centered at the mean sealevel, whereas the GPS altitude is defined above the geoidal ellipsoidas defined in WGS84 reference system.

The standard model referred to above has the form:

$P = {P_{0}\left\lbrack \frac{T_{0}}{T_{0} + {T_{grad}\left( {h - h_{0}} \right)}} \right\rbrack}^{\frac{g_{0}M}{R^{å}T_{grad}}}$

whereinP₀=Static reference pressure (Pascal)T₀=Standard temperature (Kelvin)T_(grad)=Standard temperature lapse rate (Kelvin per meter)**h₀=Reference altitude (meters)R^({dot over (a)})=Universal gas constant for airg₀=Gravitational constant M=Molar mass of Earth's air

The atmospheric model section 226 takes as inputs 240, for example, thepressure at sea level P₀ and the temperature gradient T_(grad). Theseare Kalman filter states as calibrated by the GPS altitude and verticalvelocity solutions, and are received from Kalman filter section 234.

The barometric altitude has accurate relative information, with meterlevel or less altitude noise, but not particularly accurate absolutealtitude. It is, however, quite responsive to fast altitude variations.In contrast, the GPS altitude has a good non-biased accuracy undernominal reception conditions, but has quite high altitude noise, whichcan be as large as 20 meters.

A blending filter implemented with a complementary filter architectureis used in the embodiment shown in FIG. 2 to combine both types ofinformation. The barometric altitude output 227 from atmospheric modelsection 226 is input to complementary filter 244 and passed to ahigh-pass filter 246. The GPS altitude 212 is also input tocomplementary filter 244 but passed to a low-pass filter 248. Theoutputs of both filters are summed at 250 and output as combinedGPS/Barometric altitude information at 252.

A complementary filter can be defined as a filter that receives twotypes of information and performs a filtering operation thatadvantageously uses complementary characteristics of both informationtypes to produce a single output with improved characteristics. Acomplementary filter is advantageously used when, as here, the sameinformation is available through different sensors, each of which hasquite different noise characteristics. The absolutely low frequencyaccurate information (GPS information) is filtered with a low-passfilter, and the relatively fast, accurate pressure sensor-derivedaltitude is high-pass filtered. Preferably, the response of both filtersis carefully controlled such as, for example, by ensuring that the sumof the amplitude responses of the low pass and high pass filters areexactly equal to one over the whole frequency range, effectivelyguaranteeing an “all pass” characteristic for the parameter of interest(altitude), but with differing noise processing. This technique as shownin FIG. 2 is in fact a Wiener filtering technique that works well, asthe required noise-like behavior of the signal is well-enforced (the“signal” to optimize is actually the noise on both sensors). It isworthwhile to note that this structure does not necessitate a staticposition for the receiver; the data blending occurs even if there is asignificant vertical component of motion. The order of the complementaryfilter is either first order, or can be higher order if more separationbetween the noise sources is necessary.

The complementary filter should take altitude on both sides of thefilter. The natural information from the pressure sensor is obviouslypressure, and the standard atmospheric pressure formula can be used. Itcould be used as is, as the altitude/pressure gradient of the model isnot too sensitive to P₀ and T_(grad). The P₀ is effectively eliminatedby the complementary filter.

Using certain embodiments, the absolute altitude can be measured with anaccuracy of 5 meters or less, specifically about 2-3 meters, good enoughto resolve between stacked roads. A typical example is the Bay Bridge inthe Bay Area, with an interdeck vertical distance of 10 meters. Theabsolute accuracy is good enough to unambiguously discriminate betweenlevels.

Stacked roads include two roads or freeways superposed on top of eachother, usually going in the same direction (for example, Upper and LowerWacker, in Downtown Chicago). The upper road is almost in open skyconditions, so the quality of the GNSS fix is quite good. However, aGNSS receiver mounted on a vehicle on the lower road level willexperience quite large multipath distortion, because of the metallicstructures in the immediate environment, diminishing the quality of thefix significantly.

Usually these superposed roads do not have the same exits, and the lackof knowledge concerning which road the vehicle is actually on createsmajor errors on the routing instructions (often, the user will be askedto exit at an exit that is accessible only on the other level).

Other similar situations include a freeway on a bridge, with a parallelroad running underneath or a cloverleaf structure, with an exit going upor down in altitude compared to the direct straight line. An accuratealtitude allows an early warning to the driver that he or she is on thewrong exit, even if the wrong road is running parallel to the desiredroad, too close to be safely discriminated with the GPS horizontalaccuracy only.

The vertical accuracy of a road survey using state of the art usingMobile Mapping Technologies, either terrestrial (using DGPS, INS, and360 degrees cameras for stereoscopic remote georeferencing) or airborne(using DGSP, INS, and LIDAR) is about 0.1 meters to 0.3 meters ofabsolute accuracy. This information is not necessarily inserted into themapping database as delivered to the end customer as in PersonalNavigation Devices (PND), because of unusability in the final PND. Thedescribed absolute altitude determination technique make availability ofaccurate vertical mapping information valuable, especially in the caseof stacked roads.

In order to make altitude mapping useful for such situations,embodiments of the invention provide accurate absolute altitude fordetermining along which level of stacked roads the car is actuallymoving. The reported Absolute altitude Accuracy needs to be consistentlybetter than half the minimum distance between the stacked roads (whichis about 9-10 m).

A test of an embodiment appropriate for such situations is explained inreference to FIGS. 3-5. In the presently described test, an SMD-500pressure sensor is polled at 10 hertz through a DevASys 12C-USBconverter and collected by NemeriX® GHS host software. A GPS receiverwas mounted on the roof of a test vehicle and its output was alsocollected by the NemeriX® GHS software. The software outputs logmessages containing the sensor readings and the GPS readings to a logfile that is analyzed in the laboratory using Matlab®.

The test was performed on the Bay Bridge on Interstate 80 between SanFrancisco and Alameda Counties. The length of the bridge is 23,000 feet(4.5 miles). The bridge comprises a westbound and eastbound portion. TheWest Bay Suspension Bridge has a Length 9260 feet (2822 meters), and avertical clearance of 220 feet (58 meters). The East Bay CantileverBridge has a length of 10,176 feet (3101 meters), and a verticalclearance 191 feet (67 meters). Spacing between the upper (westbound)deck and the lower (eastbound) deck is about constant at ten meters formost of the length of the bridge. Because the lower level does not havea clear view of the sky, receivers traveling eastbound experiencesignificant multipath interference.

FIG. 3 shows a graph 300 of performance of a standard GPS receiverwithout using pressure sensor data. The graph 300 shows GPS altitude(y-axis) as measured along the length of the bridge (x-axis). GPSaltitude readings for both the westbound (302) and eastbound (304) decksare shown.

The GPS receiver heading westbound has a clear view of the sky and thustracks the altitude smoothly, which results in curve 302. Because ofmultipath the Kalman filter altitude for the eastbound deck, curve 304,shows severe overshoots causing the east bound altitude to appeargreater than it actually is by more than 10 meters. This makes it verydifficult to distinguish between the two levels. At point 306, the twocures actually cross, and the eastbound deck appears to be above thewestbound deck.

FIG. 4 shows the improvement when using embodiments of the presentinvention. FIG. 4 shows a graph 400 of GPS readings along the Bay Bridgeusing pressure sensor data. Graph 400 again shows altitude readings(y-axis) against horizontal distance along the bridge (x-axis). Curve402 reflects westbound (upper deck) readings, and curve 404 reflectseastbound (lower deck) readings. These readings are the output of acomplementary filter designed in accordance with techniques disclosed inreference to FIGS. 1 and 2. As seen in FIG. 4, the lower deck readingsare consistently below the upper deck, allowing for distinction betweenthe two. It is also important to note that not only the relativeaccuracy between the upper and lower decks was improved by blendingpressure sensor data with GPS data; but also the absolute accuracy wasimproved.

For the measurements as recorded in FIG. 4, the altitude readings werecalibrated using the output of a least squares position solution, notthe output of a Kalman filtered position solution. The unfiltered leastsquares solution has high noise but no bias. The Kalman filter, on theother hand, is far smoother (less noise), but has a bias generated byits smoothing effect.

FIG. 5 shows a graph 500 of the difference 502 between measurements onthe upper and lower decks (y-axis) made with pressure sensor aided GPSreceiver according to FIG. 4, along the length of the bridge (x-axis).FIG. 5 shows that the difference is fairly constant around 10 meters,with a dip 504 in the middle where the bridge reflecting the altitudedifference of two levels at Treasure Island.

It is envisioned that the embodiments of the invention will also beuseful for pedestrian navigation in narrow and winding streets. Thisincludes so-called “last mile” navigation (navigation from the car tothe building) as well as pedestrian tourism in cities (reliableturn-by-turn navigation in very narrow streets). Such navigation can notrely on map matching techniques, since pedestrians are not constrainedto roads. Therefore, the horizontal accuracy must be better than halfthe typical distance between two streets in narrow urban environment.

By using per satellite multipath detection, flagging, and compensationas described herein based on difference between physical world (pressuresensor) and GPS measurements, effective pedestrian navigation can beimplemented. A multipath-free altitude Kalman filter is generated by GPSand pressure sensor measurement blending. This eliminates the DTM(Digital Terrain Model) based altitude aiding in existing techniques.

Multipath mitigation can be accomplished on a per satellite/perpseudo-range basis MP detection by using probability data associationtechniques. A I-D position fix can be performed using pressure sensordata and knowledge of time. A Kalman Filter is used to estimate of thevertical multipath offset for each satellite. A second horizontal Kalmanfilter is used to generate corrected/deweighted pseudo ranges. This isthe equivalent of having an extra satellite with good intermeasurementnoise characteristics located at the center of the Earth, and results ina better position more often with an extra virtual satellite (only 3satellites are required for 3-D position). Altitude aiding informationcan be inserted in the altitude aiding port of the Kalman Filter.

Using the virtual satellite provided by the pressure sensor it ispossible to determine the variation of pseudo-ranges individually persatellite, not just as a global position error. In an urban canyonenvironment, for example, there will usually be at least two satellitesthat are not affected by multipath. It is therefore possible to do a 1-Dposition fix in the vertical dimension with these two satellites, if anapproximate user horizontal position is known. The position solutionresults in an altitude and a local clock bias. The difference betweenthe predicted position and the actual position for a given satellite isthen used as the input variable to a weighting function that is used toweight the actual measured pseudorange in the Kalman filter. A I-Dposition fix can be repeated for all available satellite combinations.The satellites with multipath will be automatically rejected because ofthe weighting functions.

Once the altitude is known, a horizontal pseudorange error can becalculated, since the satellite elevation is known via ephemeris data.The horizontal pseudorange error is passed to a separate horizontalKalman filter. One can then predict using a pressure sensor the positionand clock at time t+1.

FIG. 6 shows a two Kalman filter embodiment 600 of the present inventionuseful for generating a navigation solution. Kalman filter 602 is analtitude Kalman filter, which uses pressure sensor values to produce amore accurate GNSS altitude and estimate horizontal pseudorange errors.Kalman filter 604 is a horizontal Kalman filter that produces a latitudeand longitude.

Altitude Kalman filter 602 receives pressure sensor information at input606 and pseudorange values per available satellite at input 608, andoutputs altitude information at output 610, local clock bias at output612 and horizontal pseudorange offset estimates at output 614, ascalculated, for example, using I-D vertical position fixes as describedabove. Horizontal Kalman filter 604 receives these as inputs and, usingthe data in combination with actual pseudoranges (not shown) calculatesa final latitude and longitude, which is presented to the user alongwith the filtered altitude information.

In another embodiment, the pressure sensor is simply used to detect whena sudden change in vertical position takes place for a particularsatellite, and the corresponding satellite can be blocked or deweighted.

Embodiments of the invention are also envisioned to be useful for indoornavigation, where the resolution of the pressure sensor aided navigationsolution is enough to determine which floor of a building the user ison. It is much more accurate than the GNSS solution only, that will beeven better for vertical positioning.

As it will be apparent to a person of skill in the art, theabove-described embodiments can be modified in any of a variety of wayswhile not departing from the core teachings of the invention. Theabove-described embodiments are not intended as any way to be alimitation or disclaimer of subject matter, but rather intended toinform the following claims.

1. A navigation receiver, comprising: a pressure sensor input; circuitrycoupled to the pressure sensor input; and wherein the circuitry isconfigured to use information from the pressure sensor input to improvea position fix.
 2. The navigation receiver of claim 1, furthercomprising a pressure sensor connected to the pressure sensor input; andwherein the relative measurements of the pressure sensor are moreaccurate than the absolute measurements of the pressure sensor.
 3. Thenavigation receiver of claim 1, wherein the navigation receivercomprises a GNSS receiver, and wherein the circuitry is configured tocalibrate an altitude derived from the pressure sensor input using analtitude derived from the GNSS receiver.
 4. The navigation receiver ofclaim 3, wherein the altitude derived from the GNSS receiver used tocalibrate the altitude derived from the pressure sensor input has notbeen Kalman-filtered.
 5. The navigation receiver of claim 3, wherein thecircuitry comprises a central processing unit executing a positionsolution section, a velocity solution section, a Kalman filter and anatmospheric model section.
 6. The navigation receiver of claim 2,wherein the circuitry coupled to the pressure sensor input is configuredto derive a first altitude from the pressure sensor input; and whereinthe circuitry coupled to the pressure sensor input is configured toblend the first altitude with a second altitude derived from a GNSSsensor.
 7. The navigation receiver of claim 6, wherein the circuitrycoupled to the pressure sensor input is configured to carry out acomplementary filter operation.
 8. A method for improvement of GNSSpositioning, comprising: receiving a GNSS measurement; deriving a firstaltitude from the GNSS measurement; deriving a second altitude from apressure sensor input corresponding approximately in time to the GNSSmeasurement; and combining the first and second altitude measurements toobtain a blended altitude measurement.
 9. The method of claim 8, whereinthe step of combining the first and second altitude measurementscomprises executing a complementary filter operation.
 10. The method ofclaim 9, wherein the step of executing a complementary filter operationcomprises filtering data related to the first altitude using a low-passfilter and filtering data related to the second altitude using ahigh-pass filter.
 11. The method of claim 8, wherein the second altitudeis derived using an atmospheric model.
 12. A portable navigation system,comprising: a GNSS sensor; a pressure sensor; and a microprocessorconfigured to use input derived from the GNSS sensor and the pressuresensor to provide a map location without using map-matching.
 13. Theportable navigation system of claim 12, wherein the navigation systemprovides a horizontal accuracy of 5 meters or less in an urban canyonenvironment.
 14. The portable navigation system of claim 12, furthercomprising a machine-readable medium comprising software instructionsthat, when executed by the microprocessor, would perform a methodcomprising the steps of: computing a GNSS altitude; computing analtitude based on a measurement derived from the pressure sensor; andcombining the GNSS altitude and the altitude based on a measurementderived from the pressure sensor to produce a blended altitude.
 15. Theportable navigation system of claim 14, wherein the step of combiningthe GNSS altitude and the altitude based on a measurement derived fromthe pressure sensor comprises using a complementary filter.
 16. Aportable vehicle navigation system, comprising: a GNSS sensor; apressure sensor; and a microprocessor configured to use input derivedfrom the GNSS sensor and the pressure sensor to provide a map location;and wherein the portable navigation system can differentiate between tworoads in a stacked road condition.
 17. The portable navigation system ofclaim 16, wherein the vehicle navigation system provides a verticalaccuracy of 5 meters or less under stacked road conditions.
 18. Theportable navigation system of claim 16, further comprising amachine-readable medium comprising software instructions that, whenexecuted by the microprocessor, would perform a method comprising thesteps of: computing a GNSS altitude; computing an altitude based on ameasurement derived from the pressure sensor; and combining the GNSSaltitude and the altitude based on a measurement derived from thepressure sensor to produce a blended altitude.
 19. The portablenavigation system of claim 18, wherein the step of combining the GNSSaltitude and the altitude based on a measurement derived from thepressure sensor comprises using a complementary filter.